In order to enhance stored images it is usually important to do tone correction. The need for tone correction may arise from the conditions in the original image due to limitations of the film or camera. For example, an original image may have low contrast due to underexposure.
The need for tone correction may also arise from the editing process in which portions of an original image are clipped to form a separate image. For example, a clipped image may represent a relatively dark portion of the original image and thus have low contrast as well as being too dark generally.
Typically, tone correction is achieved by the use of one or more tone correction function look up tables. For a black and white photograph, in which the image is stored as set of pixel values representing gray scale values, tone correction is effected by use of a look up table in which each input pixel of the original image is transformed to an output pixel having a corresponding value derived from the tone correction function look up table.
For a color photograph, the image is stored as three sets of pixel values representing corresponding red, green, and blue color planes. In a manner similar to the processing of a black and white image, tone correction of a color image is effected by use of red, green, and blue tone correction function look up tables in which each input pixel of each of the original image color planes is transformed to an output pixel having a corresponding value derived from the corresponding tone correction function look up table.
The problem of tone correction then reduces to the problem of determining the specific tone correction function to use with the image. Manual adjustment systems require time for the operator to manipulate the shape of the tone correction curve until the image looks right. Adjustment time is important in certain applications, such as newspaper publishing, where it may be necessary to edit 100 black and white photographs per hour during peak activity periods. Furthermore, manual adjustment systems require a skilled operator, and the results will vary depending on the skill level of the individual operator.
Automatic tone adjustment systems are well known. A typical prior art automatic tone adjustment system is shown in U.S. Pat. No. 4,931,864 to Kawamura et al. In the prior art, a histogram of gray values from the original image is compiled. Then the resulting histogram data is summed (integrated) and then normalized to extend over the desired range of gray values in order to form the tone correction function.
One difficulty with the prior art automatic tone correction systems is that the resulting tone correction curve may have tone jumps resulting from large areas of similar tone values in the original image. Sharp tone jumps in the tone correction function reduce the effectiveness of automatic tone correction.
In U.S. Pat. No. 4,677,465 to Alkofer, the shape of the histogram data is corrected towards a normal distribution having the same mean and standard deviation as the sample distributions of the original image. However, Alkofer's algorithm, which involves processing the joint probabilities between the actual sample and the normal distribution having the same mean and standard deviation as the actual sample, is not easily implemented, and there remains a need to perform fast and accurate automatic tone correction.